CS60016 AI and Ethics

(Spring Semester 2025)

Theory
Niloy Ganguly (NG) niloy@cse.iitkgp.ac.in


Teaching Assistant

KISHALAY DAS <kishalay.msit@gmail.com>
Abhilash Nandy <nandyabhilash@gmail.com>


Notices

 

Theory

        Class Room/Hour
       Books
       Evaluation
       Lectures
       Assignments
       Students List
          

Class Room/Hours

 Lectures : Mon, Tue
Room :
Units : 3-0-0
Credits : 3
Contact : Room #122 (CSE), Phone 83460

Resources


J. Kleinberg, S. Mullainathan, Simplicity Creates Inequity: Implications for Fairness, Stereotypes, and Interpretability. ACM Conference on Economics and Computation, 2019 [Paper]

Michael Feldman, Sorelle A. Friedler, John Moeller, Carlos Scheidegger, and Suresh Venkatasubramanian. Certifying and Removing Disparate Impact, KDD 2015

Fairness Constraints: Mechanisms for Fair Classification M. B. Zafar, I. Valera, M. Gomez Rodriguez and K. P. Gummadi AISTATS 2017, Fort Lauderdale, FL, April 2017. [Paper]

Equality of opportunity in supervised learning, Moritz Hardt, Eric Price, Nati Srebro, NIPS 2016 [Paper]

Pal, M., Pokhriyal, S., Sikdar, S. et al. Ensuring generalized fairness in batch classification. Sci Rep 13, 18892 (2023). [Paper]

M. Pal, S. Sikdar and N. Ganguly, "Fair Link Prediction With Overlapping Groups," in IEEE Transactions on Computational Social Systems, doi: 10.1109/TCSS.2024.3479702. [Paper] [Slides]

------------------------MIDSEM SPRING 2024-25--------------------



Fairness of Exposure in Rankings, Ashudeep Singh, Thorsten Joachims (KDD '18) [Paper] - CLASSIFICATION

FairRec: Two-Sided Fairness for Personalized Recommendations in Two-Sided Platforms Gourab K Patro*, Arpita Biswas*, Niloy Ganguly, Krishna P. Gummadi, Abhijnan Chakraborty. The Twenty-ninth Web Conference (WWW-2020) [Paper] [Slides] - RECOMMENDATION.

Incremental Fairness in Two-Sided Market Platforms: On Smoothly Updating Recommendations (AAAI '20) [Paper] [Slides] - RECOMMENDATION

Scheduling Virtual Conferences Fairly: Achieving Equitable Participant and Speaker Satisfaction (WWW '22) [Paper] [Slides] - SCHEDULING

FairFoody: Bringing in Fairness in Food Delivery (AAAI '22) [Paper] [Slides] - ASSIGNMENT

Gigs with Guarantees: Achieving Fair Wage For Food Delivery Workers (IJCAI '22) [Paper] - ASSIGNMENT

Adversarial Inter-Group Link Injection Degrades the Fairness of Graph Neural Networks (ICDM 2022) Hussain Hussain, Meng Cao, Sandipan Sikdar, Denis Helic, Elisabeth Lex, Markus Strohmaier, Roman Kern [Paper] [Slides]

Fairness Violations and Mitigation under Covariate Shift (FAccT '21), Harvineet Singh, Rina Singh, Vishwali Mhasawade, Rumi Chunara, [Paper and Slides]

----------------- EXPLAINABILITY

Benjamin Letham, Cynthia Rudin, Tyler McCormick, David Madigan; Interpretable Classifiers Using Rules and Bayesian Analysis 2015

Himabindu Lakkaraju, Stephen H. Bach, Jure Leskovec Interpretable Decision Sets: A Joint Framework for Description and Prediction

Marco Tulio Ribeiro, Sameer Singh, Carlos Guestrin Anchors: High-Precision Model-Agnostic Explanations AAAI'2018

Marco Tulio Ribeiro, Sameer Singh, Carlos Guestrin "Why Should I Trust You?" Explaining the Predictions of Any Classifier KDD 2016

Rex Ying, Dylan Bourgeois, Jiaxuan You, Marinka Zitnik, Jure Leskovec GNNExplainer: Generating Explanations for Graph Neural Networks Neurips,19

Matthew D Zeiler, Rob Fergus Visualizing and Understanding Convolutional Networks ECCV, 2014

Karen Simonyan, Andrea Vedaldi, Andrew Zisserman Deep Inside Convolutional Networks: Visualising Image Classification Models and Saliency Maps ICLR Workshop, 2014

B. Zhou, A. Khosla, L. A., A. Oliva, and A. Torralba. Learning Deep Features for Discriminative Localization. In CVPR, 2016.

Ramprasaath R Selvaraju, Abhishek Das, Ramakrishna Vedantam, Michael Cogswell, Devi Parikh, Dhruv Batra Grad-CAM: Why did you say that?

ROBUSTNESS

Christian Szegedy, Wojciech Zaremba, Ilya Sutskever, Joan Bruna, Dumitru Erhan, Ian Goodfellow, Rob Fergus Intriguing properties of neural networks ICLR (Poster) 2014

Ian Goodfellow Jonathon Shlens Christian Szegedy Explaining And Harnessing Adversarial Examples International Conference on Learning Representations (2015)

Nicolas Papernot, Patrick McDaniel, Ian Goodfellow, Somesh Jha, Z. Berkay Celik, Ananthram Swami Practical Black-Box Attacks against Machine Learning

PRIVACY - FEDERATED LEARNING

Brendan McMahan Eider Moore Daniel Ramage Seth Hampson Blaise Aguera y Arcas Communication-Efficient Learning of Deep Networks from Decentralized Data

Jianyu Wang, Gauri Joshi Adaptive Communication Strategies to Achieve the Best Error-Runtime Trade-off in Local-Update SGD

Jianyu Wang, Qinghua Liu, Hao Liang, Gauri Joshi, H. Vincent Poor Tackling the Objective Inconsistency Problem in Heterogeneous Federated Optimization

Evaluation

Lectures

Assignments

Incremental Fairness in Two-Sided Market Platforms: On Smoothly Updating Recommendations Gourab K Patro, Abhijnan Chakraborty, Niloy Ganguly, Krishna P. Gummadi. (AAAI-2020), Oral Presentation, New York, USA.

Chierichetti, F., Kumar, R., Lattanzi, S. and Vassilvitskii, S., 2017. Fair Clustering Through Fairlets. In Advances in Neural Information Processing Systems (pp. 5036-5044).

Abhisek Dash, Abhijnan Chakraborty, Saptarshi Ghosh, Animesh Mukherjee, and Krishna P. Gummadi. 2021. When the Umpire is also a Player: Bias in Private Label Product Recommendations on E-commerce Marketplaces. In Proceedings of the 2021 ACM Conference on Fairness, Accountability, and Transparency (FAccT '21). Association for Computing Machinery, New York, NY, USA, 873–884. https://doi.org/10.1145/3442188.3445944

Prerna Juneja and Tanushree Mitra. 2021. Auditing E-Commerce Platforms for Algorithmically Curated Vaccine Misinformation. In Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems (CHI '21). Association for Computing Machinery, New York, NY, USA, Article 186, 1–27. https://doi.org/10.1145/3411764.3445250

GUIDE: Group Equality Informed Individual Fairness in Graph Neural Networks Weihao Song, Yushun Dong, Ninghao Liu, Jundong Li, SIGKDD Conference on Knowledge Discovery and Data Mining (KDD 2022).

Zijian Zhang, Koustav Rudra, Avishek Anand: Explain and Predict, and then Predict Again. WSDM 2021: 418-426

Lydia T. Liu, Sarah Dean, Esther Rolf, Max Simchowitz, Moritz Hardt, Delayed Impact of Fair Machine Learning, IJCAI

Dennis Wei, Karthikeyan Natesan Ramamurthy, Flavio P. Calmon, Optimized Score Transformation for Consistent Fair Classification, JMLR 22(258):1−78, 2021

Jialu Wang, Xin Eric Wang, Yang Liu, Understanding Instance-Level Impact of Fairness Constraints, ICML 2022

Guanhua Zhang, Yihua Zhang, Yang Zhang, Wenqi Fan, Qing Li, Sijia Liu, Shiyu Chang, Fairness Reprogramming, Neurips 2022.

Maarten Buyl, Tijl De Bie, Optimal Transport of Classifiers to Fairness, Neurips 2022 Yuji Roh, Kangwook Lee, Steven Euijong Whang, Changho Suh, FairBatch: Batch Selection for Model Fairness, ICLR 2021